Read Chinese Introduction：Here！

• 2021.1.8 The train and test codes are released.

• pytorch1.7
• python>=3.6

• You can modify hparam.py to determine whether 2D or 3D segmentation and whether multicategorization is possible.
• We provide algorithms for almost all 2D and 3D segmentation.
• This repository is compatible with almost all medical data formats(e.g. nii.gz, nii, mhd, nrrd, ...), by modifying fold_arch in hparam.py of the config.

• without pretrained-model

python train.py

• with pretrained-model

python train.py -k True

• testing

python test.py

• 2D
  • unet
  • unet++
  • miniseg
  • segnet
  • pspnet
  • highresnet(copy from https://github.com/fepegar/highresnet, Thank you to fepegar for your generosity!)
  • deeplab
  • fcn
• 3D
  • unet3d
  • densevoxelnet3d
  • fcn3d
  • vnet3d
  • highresnert(copy from https://github.com/fepegar/highresnet, Thank you to fepegar for your generosity!)
  • densenet3d

This project is not perfect and there are still many problems. If you are using this project and would like to give the author some feedback, you can send Kangneng Zhou an email.

This repository is an unoffical PyTorch implementation of Medical segmentation in 3D and 2D and highly based on MedicalZooPytorch and torchio.Thank you for the above repo. Thank you to Cheng Chen, Daiheng Gao, Jie Zhang and Xing Tao for all the help I received.